CN109861728B - Joint multi-relay selection and time slot resource allocation method for large-scale MIMO system - Google Patents

Joint multi-relay selection and time slot resource allocation method for large-scale MIMO system Download PDF

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CN109861728B
CN109861728B CN201910128413.0A CN201910128413A CN109861728B CN 109861728 B CN109861728 B CN 109861728B CN 201910128413 A CN201910128413 A CN 201910128413A CN 109861728 B CN109861728 B CN 109861728B
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高洪元
苏雨萌
张世铂
刁鸣
孙志国
杜亚男
马雨微
谢婉婷
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Harbin Engineering University
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Abstract

The invention relates to a combined multi-relay selection and time slot resource allocation method of a large-scale MIMO system, which combines the advantages of a quantum optimization mechanism and a termite colony optimization mechanism, solves the complex mixed optimization problem of multi-relay selection and time slot resource allocation of a Massive MIMO system by using the quantum termite colony optimization method, and has the advantages of high search speed and strong global search capability. The invention combines the wireless energy acquisition technology, can obviously reduce the energy consumption in the information transmission process of the Massive MIMO cooperative communication system, and respectively sends interference signals to the eavesdropper through the user terminal and the interference relay so as to reduce the signal-to-interference-and-noise ratio of the eavesdropper, thereby effectively improving the secrecy capacity of the Massive MIMO system and ensuring the safety and the reliability of the communication system.

Description

Joint multi-relay selection and time slot resource allocation method for large-scale MIMO system
Technical Field
The invention relates to a combined multi-relay selection and time slot resource allocation method of a large-scale MIMO (massive MIMO) system, belonging to the key technical field of 5G mobile communication.
Background
With the rapid development of information technology, mobile communication is promoting social revolution in an unprecedented way. Compared with a 4G network, the 5G network can meet higher data transmission requirements, has wider wireless communication coverage and better communication quality, and can construct a new scene of all things interconnection. However, with the rapid development of 5G communication, the problems of greenhouse gas emission and energy consumption caused by mobile communication are becoming more serious, and green communication has become a research hotspot of 5G communication for realizing sustainable development. As one of the 5G key technologies, Massive MIMO can obviously reduce energy consumption, and a base station is provided with a large number of antennas and can serve more terminals at the same time, so that the frequency spectrum efficiency and the energy efficiency of a system are effectively improved, and the system capacity is further improved. The relay network is utilized to assist data transmission of a Massive MIMO system, so that the coverage area of the communication system can be enlarged, and the communication quality of cell edge users can be obviously improved. Due to the influence of different geographical positions and channel fading, the ability of each relay to improve the communication quality of users is different, and how to select a proper relay to assist the data transmission of cell edge users has important significance for improving the capacity of a Massive MIMO system.
Due to the openness of wireless transmission, the existence of the eavesdropper can easily intercept useful information of a communication system, and information leakage is caused. Therefore, improving the security of the communication system has become one of the research hotspots of the Massive MIMO system on the premise of ensuring the user communication quality. With the increasing shortage of resources, how to establish a data transmission mechanism which is environment-friendly, high in communication quality and strong in confidentiality becomes a difficult problem to be solved urgently in a Massive MIMO system. Through the search of the existing documents, it is found that a Source node and a Relay Power distribution method of a Massive MIMO system with limited energy are proposed by 'heated Green and Secure Communications over Massive MIMO Relay Networks' published by Jianan Chen et al in IEEE Access (2017, Vol.5, pp.869-880), but Relay selection is not involved, information leakage when the Source node transmits information to the Relay is ignored, and in addition, other anti-eavesdropping strategies are not utilized to improve the confidentiality of information transmission. "Joint Relay Selection and Power Allocation in Large-Scale MIMO Systems With unknown Relay and eavesdropper" published by Ali Kuhestani et al in IEEE Transactions on Information strategies and Security (2018, Vol.13, No.2, pp.341-355) proposes a Relay Selection and Power Allocation method under the situation of coexistence of unknown Relays and Eavesdroppers. Under the condition that the total power of the system is fixed, a user sends an interference signal to the eavesdropper in the information transmission process of the base station so as to reduce the interception capability of the eavesdropper on useful information, and in the relay transmission stage, the user terminal improves the signal-to-interference-and-noise ratio of the user terminal by a self-interference elimination method, so that the confidentiality capacity of a Massive MIMO system is improved. But this approach only involves a single relay selection problem, with multiple candidate relays in an idle state. The existing literature indicates that the data transmission mechanism of the existing Massive MIMO cooperative communication system needs the external power to be continuously provided, and the resource utilization rate is low, so that it is difficult to obtain a large secret capacity in the actual communication system. Therefore, establishing a new data transmission mechanism has important significance in improving the security capacity of the system while minimizing energy consumption. The invention designs a combined multi-relay selection and time slot resource allocation method of a Massive MIMO system, which combines the advantages of a Quantum Optimization mechanism and a Termite Colony Optimization (TCO) mechanism, solves the complex mixed Optimization problem of multi-relay selection and time slot resource allocation of the Massive MIMO system by utilizing a Quantum-induced Termite Colony Optimization (QTCO) method, and has the advantages of high search speed and strong global search capability. In addition, the invention combines the wireless energy acquisition technology, can obviously reduce the energy consumption in the information transmission process of the Massive MIMO cooperative communication system, and respectively sends interference signals to the eavesdropper through the user terminal and the interference relay so as to reduce the signal-to-interference-and-noise ratio of the eavesdropper, thereby effectively improving the secrecy capacity of the Massive MIMO system and ensuring the safety and the reliability of the communication system.
Disclosure of Invention
Aiming at the prior art, the technical problem to be solved by the invention is to provide a combined multi-relay selection and time slot resource allocation method of a Massive MIMO system, which has the advantages of high search speed and strong global search capability, obviously reduces energy consumption in the information transmission process of the Massive MIMO cooperative communication system, effectively improves the secrecy capacity of the Massive MIMO system, and ensures the safety and reliability of the communication system.
In order to solve the above technical problem, the present invention provides a method for combining multiple relay selection and time slot resource allocation in a large-scale MIMO system, comprising the following steps:
the method comprises the following steps: establishing a Massive MIMO cooperative communication system model, which specifically comprises the following steps:
the Massive MIMO cooperative communication system consists of a base station configured with M antennas, a user, L amplification-forwarding half-duplex relays and an eavesdropper, wherein the user, each relay and the eavesdropper are single-antenna equipment and share an authorized frequency band with a bandwidth of B with the base station, and all noises are assumed to be power spectral density of N0White Gaussian noise, the noise power σ2＝BN0Each frame of information transmission is divided into two different time slots: TS1 and TS2, when TS1 is the base station sending a signal to the relay, and the user sending an interference signal to the eavesdropper while the base station is transmitting information, then the j (j ═ 1,2, …, L) th relay rjReceived signal yjComprises the following steps:
Figure BDA0001974390670000021
wherein s isBSAnd suSignals of specific energy, p, transmitted for base stations and usersBSAnd puIs the transmit power of the base station and the user, w is the precoding matrix of the base station, ()HDenotes the conjugate transpose, hBS,jIndicating base station to relay rjChannel state information of hj,uIs a relay rjChannel state information to the user, njIs a relay rjReceived noise, will yjNormalization yields:
Figure BDA0001974390670000022
the method comprises the following steps of obtaining a vector norm by using a vector, and obtaining a variable modulus by using a linear equation, | to | represents the norm of the vector;
at TS1, the signal received by the eavesdropper
Figure BDA0001974390670000023
Comprises the following steps:
Figure BDA0001974390670000031
wherein h isBS,eIndicating channel state information from base station to eavesdropper, hu,eFor the channel state information of the user to the eavesdropper,
Figure BDA0001974390670000032
for the noise received by the eavesdropper at TS1, the signal-to-interference-and-noise ratio received by the eavesdropper during the transmission of information by the base station
Figure BDA0001974390670000033
Comprises the following steps:
Figure BDA0001974390670000034
when the base station transmits information, each relay collects the energy of the wireless signal received by the relay, and the relay rjEnergy E collected at TS1jComprises the following steps:
Ej＝η(pBS||wHhBS,j||2+pu|hj,u|2+σ2)αT
wherein eta is the energy acquisition rate, alpha is the time slot distribution coefficient, and T is the subframe duration of information transmission;
at TS2, some relays forward the signal received at TS1 to the user, the remaining relays send interfering signals to the eavesdropper, and the selection vector b is ═ b through 0-1 relays1,b2,…,bL]Indicates the relay selection result if bjWhen 1, relay r is selectedjCarrying out data transmission, and sending a signal to a user by using the energy collected by the relay at the TS 1; if b isjWhen r is 0, then relay rjSending an interference signal to an eavesdropper; the total interference generated by the relay to the user must not exceed the interference threshold I of the userthRelay rjThe power control strategy employed is:
Figure BDA0001974390670000035
the method comprises the following steps that min { } represents the minimum value in a group of numbers, and N represents the total number of relays which send interference signals to an eavesdropper;
signals received by eavesdroppers at TS2
Figure BDA0001974390670000036
Comprises the following steps:
Figure BDA0001974390670000037
wherein h isj,eAnd hk,eAre respectively a relay rjAnd relay rk(k ≠ 1,2, …, L, k ≠ j) channel status information to eavesdroppers，skIs a relay rkThe interference signal that is transmitted is,
Figure BDA0001974390670000038
for noise received by the eavesdropper at TS2, let
Figure BDA0001974390670000039
Selecting a variable b for relaying withkThe relevant variable, if bk＝0，
Figure BDA00019743906700000310
If b isk＝1，
Figure BDA00019743906700000311
Signal-to-interference-and-noise ratio received by eavesdropper in process of relay transmission information
Figure BDA00019743906700000312
Comprises the following steps:
Figure BDA00019743906700000313
wherein p isTSThe sum of the interference signal power received by the eavesdropper at TS2 can be specifically expressed as:
Figure BDA0001974390670000041
therefore, the signal-to-interference-and-noise ratio received by the eavesdropper in the information transmission process from the base station to the user is as follows:
Figure BDA0001974390670000042
wherein max {. means taking the maximum value of a set of numbers;
at TS2, by the self-interference cancellation method, the signal received by the user is:
Figure BDA0001974390670000043
wherein h isk,uIs a relay rkChannel state information to the user, nuFor the noise received by the user, the signal-to-interference-and-noise ratio gamma received by the user in the process of relaying the informationuComprises the following steps:
Figure BDA0001974390670000044
the secret capacity of the Massive MIMO relay system is:
R(b,α)＝max{(1-α)B[log2(1+γu)-log2(1+γe)],0}
step two: initializing quantum termite colony and system parameters, specifically:
setting the number of quantum termites in a quantum termite colony as H, the dimension of the positions of the quantum termites as D, wherein the D represents the dimension of the problem to be solved, binary bit coding is adopted for discrete variables to be solved, K binary bit coding is adopted for continuous variables, and then for the problem of joint multi-relay selection and time slot resource allocation of a Massive MIMO system, the dimension D of the positions of the quantum termites is L + K; using t to represent iteration times, the quantum position of the ith iteration of the i-th quantum termite is
Figure BDA0001974390670000045
Wherein the content of the first and second substances,
Figure BDA0001974390670000046
1,2, …, H, D1, 2, …, D; position of ith Quantum Termite
Figure BDA0001974390670000047
Can be obtained by measuring the quantum position of the quantum well, and the measurement equation is as follows:
Figure BDA0001974390670000048
wherein the content of the first and second substances,
Figure BDA0001974390670000049
is [0,1 ]]At the beginning, let t equal to 0, the initial position of quantum termite i is
Figure BDA00019743906700000410
The local optimum position of quantum termite i is
Figure BDA00019743906700000411
Step three: calculating an adaptive value of the position of the quantum termite, specifically:
position of ith quantum termite for the t iteration
Figure BDA00019743906700000412
Mapping to vector needing optimization of Massive MIMO cooperative communication system
Figure BDA00019743906700000413
Namely, the scheme of multi-relay selection and time slot resource allocation of the Massive MIMO system passes through the fitness function
Figure BDA00019743906700000414
Calculating the adaptive value of the quantum termite, wherein exp represents an exponential function, analyzing the adaptive values of all the quantum termites in the quantum termite colony, and recording the position with the minimum adaptive value searched by the ith quantum termite so far as a local optimal position
Figure BDA0001974390670000051
Marking the position with the minimum adaptive value searched by the whole quantum termite colony as the global optimal position
Figure BDA0001974390670000052
Step four: according to the evolution rule, the quantum position and the position of the quantum termite are updated, specifically:
pheromones of Quantum Termite are adaptiveFunction of the fitness value of Quantum Termite i according to
Figure BDA0001974390670000053
Conversion to the corresponding pheromone content
Figure BDA0001974390670000054
Figure BDA0001974390670000055
Wherein rho is [0,1 ]]As the evaporation rate of the pheromone, the evaporation rate,
Figure BDA0001974390670000056
for the pheromone content of the position where the quantum termite i is located in the last iteration, the quantum termite i obtains the corresponding learning neighborhood according to the following rule:
Figure BDA0001974390670000057
wherein the content of the first and second substances,
Figure BDA0001974390670000058
a set of labels for the ith quantum termite learning neighborhood,
Figure BDA0001974390670000059
for the dynamic search radius of the ith quantum termite,
Figure BDA00019743906700000510
the d-dimensional position of quantum termite l,
Figure BDA00019743906700000511
the pheromone content of the quantum termite l is represented, the number of the labels in the learning neighborhood label set of the quantum termite i represents the number of the quantum termite in the learning neighborhood of the quantum termite i, and the position of the quantum termite with the largest pheromone content in the learning neighborhood of the quantum termite i is recorded as the pheromone content of the quantum termite l
Figure BDA00019743906700000512
Quantum termite i evolves according to the following rules:
Figure BDA00019743906700000513
Figure BDA00019743906700000514
wherein the content of the first and second substances,
Figure BDA00019743906700000515
for the d-dimension quantum rotation angle of the ith quantum termite in the updated quantum termite colony,
Figure BDA00019743906700000516
for the updated d-dimensional quantum position of quantum termite i,
Figure BDA00019743906700000517
the data is an empty set,
Figure BDA00019743906700000518
is [0,1 ]]Uniform random number in between, epsilon is the variation probability of quantum position when quantum rotation angle is 0, abs (one.) represents absolute value, c1、c2、c3、c4、c5Is an influencing factor; c. C1、c2、c3Respectively representing the influence degrees of the local optimal position, the position with the maximum pheromone content in the learning neighborhood and the global optimal position of the ith quantum termite on the quantum rotation angle when the learning neighborhood is not empty; c. C4And c5Representing the influence degree of the local optimal position and the global optimal position of the ith quantum termite on the quantum rotation angle when the learning neighborhood is empty;
obtaining the updated position of the quantum termite i through a measurement equation according to the updated quantum position of the quantum termite i;
step five: according toMapping the rule to obtain new quantum termite i position
Figure BDA00019743906700000519
Corresponding multiple relay selection and timeslot resource configuration vectors
Figure BDA00019743906700000520
Calculating the updated adaptive value of the quantum termite, and updating the local optimal position of the quantum termite through a greedy selection mechanism, wherein the process is as follows:
Figure BDA00019743906700000521
recording the local optimal position with the minimum adaptive value in the updated quantum termite colony as the global optimal position
Figure BDA00019743906700000522
Step six: if the iteration times are less than the preset maximum iteration times, making t equal to t +1, and returning to the fourth step; otherwise, stopping iteration and outputting the global optimal position of the quantum termite colony
Figure BDA0001974390670000061
And obtaining the optimal combined multi-relay selection and time slot resource allocation scheme of the Massive MIMO system through the mapping rule.
The invention has the beneficial effects that: compared with the prior art, the Massive MIMO system combined multi-relay selection and time slot resource allocation method fully considers the influence of the existence of the eavesdropper on the communication system and the influence of the interference caused by part of relays on the user, and has the following advantages:
1. the invention designs a new Massive MIMO system data transmission mechanism, and the relay network converts the received wireless signal energy into the self sending power through the energy acquisition technology, thereby obviously reducing the energy consumption in the information transmission process of the cooperative communication network and effectively improving the energy utilization rate of the communication system. At different stages of data transmission, interference signals are respectively sent to the eavesdropper through the user and part of relays, so that the interception capability of the eavesdropper on information can be effectively reduced, and a new anti-eavesdropping strategy is provided for a Massive MIMO system.
2. The invention fully considers the influence of the existence of the eavesdropper on the confidentiality of the Massive MIMO system and the influence of interference caused by partial relays on users, and the designed combined multi-relay selection and time slot resource allocation method can effectively balance the communication quality, the system energy consumption and the resource utilization rate of the users, breaks through the limitation of the traditional Massive MIMO relay selection and resource allocation method, and can better meet the requirements of actual engineering.
3. The quantum termite colony optimization method designed by the invention combines the advantages of a quantum optimization mechanism and a termite colony optimization mechanism, makes full use of information exchange between quantum termites, and has the characteristics of high convergence rate and strong global search capability. For the complex mixed optimization problem of Massive MIMO multi-relay selection and time slot resource allocation, the maximum system secret capacity can be obtained by adopting the quantum termite colony optimization method designed by the invention.
4. Compared with the traditional ant colony optimization method, the quantum ant colony optimization method designed by the invention has stronger optimization capability, breaks through the limitation that the ant colony optimization method is only suitable for solving continuous problems, provides a new solving method for complex hybrid optimization problems, can be transplanted to other complex engineering problems, and has good popularization.
Drawings
Fig. 1 is a flowchart of a combined multi-relay selection and timeslot resource allocation method for a quantum termite colony of a Massive MIMO system;
FIG. 2 is a flow chart of a quantum termite colony optimization mechanism;
FIG. 3 is a curve of system secret capacity as a function of iteration times for a combined multi-relay selection and time slot resource allocation method employing quantum termite colony, and particle swarm optimization mechanisms;
FIG. 4 is a plot of system privacy capacity as a function of relay number for a combined multi-relay selection and timeslot resource allocation method employing quantum termite, termite and particle swarm optimization mechanisms;
FIG. 5 is a curve of system secret capacity varying with user transmission power using a joint multi-relay selection and timeslot resource allocation method with quantum termite colony, and particle swarm optimization mechanisms;
FIG. 6 is a curve of system secret capacity varying with base station transmission power using a combined multi-relay selection and time slot resource allocation method of quantum termite colony, termite colony and particle swarm optimization mechanisms;
fig. 7 is a curve of the system secrecy capacity varying with the user interference threshold in the method of combining multi-relay selection and time slot resource allocation using quantum termite colony, termite colony and particle swarm optimization mechanisms.
Detailed Description
The following further describes the embodiments of the present invention with reference to the drawings.
The invention designs a combined multi-relay selection and time slot resource allocation method for a Massive MIMO system quantum termite colony, which combines the advantages of a quantum optimization mechanism and a termite colony optimization mechanism, has the advantages of high search speed and strong global search capability, and is realized by the following steps: firstly, establishing a Massive MIMO cooperative communication system model; initializing quantum termite colony and system parameters, and obtaining the position of the quantum termite through measurement rules; the third step: calculating an adaptive value of the position of the quantum termite to obtain a local optimal position of the quantum termite and a global optimal position of the quantum termite group; the fourth step: updating the quantum position and the position of the quantum termite according to the evolution rule; the fifth step: calculating the updated adaptive value of the quantum termite, and updating the local optimal position of the quantum termite and the global optimal position of the quantum termite colony; and a sixth step: if the iteration times are less than the preset maximum iteration times, returning to the fourth step; otherwise, terminating iteration, outputting the global optimal position of the quantum termite colony, and obtaining the optimal combined multi-relay selection and time slot resource allocation scheme of the Massive MIMO system through the mapping rule. The method for combining multi-relay selection and time slot resource allocation can remarkably reduce energy consumption in the information transmission process of the Massive MIMO cooperative communication system, effectively balance user communication quality, system energy consumption and resource utilization rate, remarkably improve the secrecy capacity of the system, and can meet the requirements of actual engineering better.
The invention aims to provide a quantum-ant-colony-based Massive MIMO system combined multi-relay selection and time slot resource allocation method aiming at the defects of the data transmission mechanism of the existing Massive MIMO system.
As shown in fig. 1, the method for joint multi-relay selection and timeslot resource allocation of quantum termite colony in Massive MIMO system designed by the present invention includes the following steps:
step one, establishing a Massive MIMO cooperative communication system model
The Massive MIMO cooperative communication system consists of a base station provided with M antennas, a user, L amplification-and-forwarding (AF) half-duplex relays and an eavesdropper. The user, each relay and the eavesdropper are single-antenna equipment and share the authorized frequency band with the bandwidth B with the base station. Assuming that all noise is power spectral density N0White Gaussian noise, the noise power σ2＝BN0. Each frame of information transmission may be divided into two different Time Slots (TS): TS1 and TS 2. At TS1, when the base station sends a signal to the relay and the user sends an interference signal to the eavesdropper while the base station transmits information, the j (j ═ 1,2, …, L) th relay rjReceived signal yjComprises the following steps:
Figure BDA0001974390670000071
wherein s isBSAnd suSignals of specific energy, p, transmitted for base stations and usersBSAnd puIs the transmit power of the base station and the user, w is the precoding matrix of the base station, ()HDenotes the conjugate transpose, hBSAnd j represents base station to relay rjChannel state information of hj,uIs a relay rjChannel state information to the user, njIs a relay rjThe received noise. Will yjNormalization, one can obtain:
Figure BDA0001974390670000081
wherein, | | represents solving the norm of the vector, |.
At TS1, the signal received by the eavesdropper
Figure BDA0001974390670000082
Comprises the following steps:
Figure BDA0001974390670000083
wherein h isBS,eIndicating channel state information from base station to eavesdropper, hu,eFor the channel state information of the user to the eavesdropper,
Figure BDA0001974390670000084
noise received at TS1 for an eavesdropper. Signal-to-interference-and-noise ratio received by eavesdropper in information transmission process of base station
Figure BDA0001974390670000085
Comprises the following steps:
Figure BDA0001974390670000086
when the base station transmits information, each relay collects the energy of the wireless signal received by the relay, and the relay rjEnergy E collected at TS1jComprises the following steps:
Ej＝η(pBS||wHhBS,j||2+pu|hj,u|2+σ2)αT
wherein, η is the energy collection rate, α is the time slot distribution coefficient, and T is the subframe duration of information transmission.
At TS2, some relays forward the signal received at TS1 to the user, and the remaining relays send interfering signals to the eavesdropper. Selecting vector b ═ b through 0-1 relay1,b2,…,bL]Indicates the relay selection result if bjWhen 1, relay r is selectedjCarrying out data transmission, and sending a signal to a user by using the energy collected at TS 1; if b isjWhen r is 0, then relay rjAn interfering signal is transmitted to the eavesdropper. The relay sends interference signals to the eavesdropper and simultaneously generates interference to the user, and in order to ensure the communication quality of the user, the total interference of the relay does not exceed the interference threshold I of the userthThus, the following simple power control strategy may be employed:
Figure BDA0001974390670000087
and the min {. means that the minimum value in a group of numbers is taken, and the N represents the total number of relays which send interference signals to the eavesdropper.
So the eavesdropper receives the signal at TS2
Figure BDA0001974390670000088
Comprises the following steps:
Figure BDA0001974390670000089
wherein h isj,eAnd hk,eAre respectively a relay rjAnd relay rk(k ≠ 1,2, …, L, k ≠ j) channel status information to eavesdropper, skIs a relay rkThe interference signal that is transmitted is,
Figure BDA00019743906700000810
noise received at TS2 for an eavesdropper. Order to
Figure BDA00019743906700000811
Selecting a variable b for relaying withkThe relevant variable, if bk＝0，
Figure BDA00019743906700000812
If b isk＝1，
Figure BDA00019743906700000813
Signal-to-interference-and-noise ratio received by eavesdropper in process of relay transmission information
Figure BDA0001974390670000091
Comprises the following steps:
Figure BDA0001974390670000092
wherein p isTSThe sum of the interference signal power received by the eavesdropper at TS2 can be specifically expressed as:
Figure BDA0001974390670000093
therefore, the signal-to-interference-and-noise ratio received by the eavesdropper in the information transmission process from the base station to the user is as follows:
Figure BDA0001974390670000094
wherein max represents taking the maximum value of a set of numbers.
At TS2, by the self-interference cancellation method, the signal received by the user is:
Figure BDA0001974390670000095
wherein h isk,uIs a relay rkChannel state information to the user, nuNoise received for the user. Signal-to-interference-and-noise ratio gamma received by user in process of relay transmitting informationuComprises the following steps:
Figure BDA0001974390670000096
therefore, the secret capacity of the Massive MIMO relay system is:
R(b,α)＝max{(1-α)B[log2(1+γu)-log2(1+γe)],0}
initializing quantum termite colony and system parameters
Setting the number of quantum termites in a quantum termite colony as H, setting the dimension of the positions of the quantum termites as D, representing the dimension of the problem to be solved, adopting binary bit coding for discrete variables to be solved, adopting K binary bit coding for continuous variables, and solving the problem of joint multi-relay selection and time slot resource allocation of a Massive MIMO system, wherein the dimension D of the positions of the quantum termites is L + K. The number of iterations is represented by t, and the quantum position of the ith iteration of the ith quantum termite can be represented as
Figure BDA0001974390670000097
Wherein the content of the first and second substances,
Figure BDA0001974390670000098
i is 1,2, …, H, D is 1,2, …, D. Position of ith Quantum Termite
Figure BDA0001974390670000099
Can be obtained by measuring the quantum position of the quantum well, and the measurement equation is as follows:
Figure BDA00019743906700000910
wherein the content of the first and second substances,
Figure BDA0001974390670000101
is [0,1 ]]A uniform random number in between. Initially, let t equal to 0 and the initial position of quantum termite i be
Figure BDA0001974390670000102
The local optimum position of quantum termite i is
Figure BDA0001974390670000103
Step three, calculating the adaptive value of the position of the quantum termite
Subjecting the ith quantum termitePosition of the t-th iteration
Figure BDA00019743906700001024
Mapping to vector needing optimization of Massive MIMO cooperative communication system
Figure BDA0001974390670000104
Namely, the scheme of multi-relay selection and time slot resource allocation of the Massive MIMO system passes through the fitness function
Figure BDA0001974390670000105
An adapted value for quantum termites is calculated, where exp { } represents an exponential function. Analyzing the adaptive values of all quantum termites in the quantum termite colony, and recording the position with the minimum adaptive value searched by the ith quantum termite as a local optimal position
Figure BDA0001974390670000106
Marking the position with the minimum adaptive value searched by the whole quantum termite colony as the global optimal position
Figure BDA0001974390670000107
Step four, updating the quantum position and the position of the quantum termite according to the evolution rule
In the updating process of the quantum termite colony, the search direction of the quantum termite colony is influenced by the content of the pheromone. The pheromone of the quantum termite is a function related to the adaptive value, and the adaptive value of the quantum termite i is determined according to the following formula
Figure BDA0001974390670000108
Conversion to the corresponding pheromone content
Figure BDA0001974390670000109
Figure BDA00019743906700001010
Wherein rho is [0,1 ]]As the evaporation rate of the pheromone, the evaporation rate,
Figure BDA00019743906700001011
the pheromone content of the position of the quantum termite i in the last iteration is shown. Quantum termite i obtains its corresponding learning neighborhood according to the following rules:
Figure BDA00019743906700001012
wherein the content of the first and second substances,
Figure BDA00019743906700001013
a set of labels for the ith quantum termite learning neighborhood,
Figure BDA00019743906700001014
for the dynamic search radius of the ith quantum termite,
Figure BDA00019743906700001015
the d-dimensional position of quantum termite l,
Figure BDA00019743906700001016
for the pheromone content of the position of quantum termite l, the learning neighborhood labels of quantum termite i are integrated with a plurality of labels, and the learning neighborhood has a plurality of corresponding quantum termites. Recording the position of quantum termite with maximum pheromone content in quantum termite i learning neighborhood as
Figure BDA00019743906700001017
Quantum termite i evolves according to the following rules:
Figure BDA00019743906700001018
Figure BDA00019743906700001019
wherein the content of the first and second substances,
Figure BDA00019743906700001020
for the d-dimension quantum rotation angle of the ith quantum termite in the updated quantum termite colony,
Figure BDA00019743906700001021
for the updated d-dimensional quantum position of quantum termite i,
Figure BDA00019743906700001022
the data is an empty set,
Figure BDA00019743906700001023
is [0,1 ]]Uniform random number in between, epsilon is the variation probability of quantum position when quantum rotation angle is 0, abs (one.) represents absolute value, c1、c2、c3、c4、c5Is an influencing factor. c. C1、c2、c3Respectively representing the influence degrees of the local optimal position, the position with the maximum pheromone content in the learning neighborhood and the global optimal position of the ith quantum termite on the quantum rotation angle when the learning neighborhood is not empty; c. C4And c5And the influence degree of the local optimal position and the global optimal position of the ith quantum termite on the quantum rotation angle is represented when the learning neighborhood is empty.
And obtaining the updated position of the quantum termite i by a measurement equation according to the updated quantum position of the quantum termite i.
Step five, obtaining the new position of the quantum termite i according to the mapping rule
Figure BDA0001974390670000111
Corresponding multiple relay selection and timeslot resource configuration vectors
Figure BDA0001974390670000112
Calculating the updated adaptive value of the quantum termite, and updating the local optimal position of the quantum termite through a greedy selection mechanism, wherein the process is as follows:
Figure BDA0001974390670000113
recording the local optimal position with the minimum adaptive value in the updated quantum termite colony as the global optimal position
Figure BDA0001974390670000114
Step six, if the iteration times are less than the preset maximum iteration times, making t equal to t +1, and returning to the step four; otherwise, stopping iteration and outputting the global optimal position of the quantum termite colony
Figure BDA0001974390670000115
And obtaining the optimal combined multi-relay selection and time slot resource allocation scheme of the Massive MIMO system through the mapping rule.
The beneficial effects of the invention are further illustrated by simulation experiments:
for the Massive MIMO relay system, the number of base station antennas M is set to 64, the base station coordinates are located at (0,0) M, the user is located at (1200,0) M, the eavesdropper is located at (1000,0) M, and each relay is randomly distributed in an area with a circle center of (500,0) M and a radius of 200M. The base station adopts a Maximum Ratio Transmission (MRT) mode for precoding, the energy acquisition rate eta is 0.8, and the system bandwidth B is 1 MHz. Assuming that all channel state information is known, all noise is at a power spectral density of N0White gaussian noise, noise power spectral density N0130dBW/Hz, wherein 1dBW 101/10W。
The parameter setting of the combined multi-relay selection and time slot resource allocation method of the Massive MIMO system quantum termite colony is as follows: and the number H of the quantum termites in the quantum termite colony is 20, the number K of the continuous variable encoding bits is 15, the dimension D of the quantum termites is related to the relay number L, and D is L + K. The pheromone evaporation rate rho is 0.8, the dynamic search radius of each quantum termite is linearly decreased from 3 to 1 along with the increase of the iteration times, the pheromones at the initial positions of the quantum termites are 0, and the influence factor c is1＝0.06，c2＝0.03，c3＝0.01，c4＝0.06，c5The mutation probability epsilon is 0.03, 0.1/K. To facilitate comparisonA joint multi-relay selection and time slot resource allocation method of Quantum-interpolated Termite Colony Optimization (QTCO), Termite Colony Optimization (TCO) and Particle Swarm Optimization (PSO) mechanisms is adopted, the population sizes of the three are set to be the same, the number of termination iterations is set to be 500, and all simulation results are the average of 200 simulations. Other parameter settings for the white ant Colony Optimization method refer to "Termite Colony Optimization: A Novel Approach for Optimizing Continuous pages", published by Ramin Hedayatzadeh et al at the 18 th International ICEE conference, and other parameter settings for the Particle Swarm Optimization method refer to "Enhancing Comprehensive Learning partition Optimization with Local Optima Topology", published by Kai Zhang et al at Information Sciences (2019, Vol.471, pp.252-268).
Fig. 3 to 7 are curves of system secret capacity varying with iteration times, relay number, user transmission power, base station transmission power and user interference threshold by adopting a quantum termite colony, termite colony and particle swarm optimization mechanism combined multi-relay selection and time slot resource allocation method. In fig. 3, the relay number L is 12, and the base station transmission power pBSUser transmit power p of 5dBWu5dBW, user interference threshold Ith-20 dBW. In FIG. 4, the number of base stations varies from 10 to 20. In fig. 5, the user transmission power puVarying from-10 dBW to 10 dBW. In FIG. 6, the base station transmits a power pBSVarying from-10 dBW to 10 dBW. In fig. 7, the user interference threshold IthThe range of-50 dBm to-10 dBm is changed, wherein 1W-10 dBW-30 dBm.
FIG. 3 is a graph of secret capacity of a Massive MIMO system as a function of iteration number. The simulation result can obviously learn that the optimizing capability and the convergence speed of the quantum termite colony optimization mechanism are obviously superior to those of the termite colony and the particle swarm optimization mechanism, and the combined multi-relay selection and time slot resource allocation method adopting the quantum termite colony optimization mechanism can obtain larger system secret capacity than other two methods.
Fig. 4 is a graph of secret capacity of Massive MIMO system as a function of the number of relays. The simulation result can obviously show that the system secret capacity of the combined multi-relay selection and time slot resource allocation method adopting the quantum termite colony optimization mechanism is increased along with the increase of the number of relays, and the performance of the combined multi-relay selection and time slot resource allocation method adopting the termite colony optimization mechanism and the particle swarm optimization mechanism is unstable, so that the larger system secret capacity is difficult to obtain. With the increase of the number of relays, the performance of the quantum termite colony combined multi-relay selection and time slot resource allocation method is better than that of the termite colony and particle swarm combined multi-relay selection and time slot resource allocation method.
Fig. 5 is a graph of secret capacity of a Massive MIMO system as a function of user transmit power. The simulation result can obviously know that the system secret capacity is increased along with the increase of the user sending power, and the performance of the quantum termite colony combined multi-relay selection and time slot resource allocation method is obviously superior to that of the termite colony and particle swarm combined multi-relay selection and time slot resource allocation method.
FIG. 6 is a graph of secret capacity of Massive MIMO system as a function of base station transmit power. The simulation result can obviously know that the system secret capacity is obviously improved along with the increase of the sending power of the base station, and the performance of the quantum termite colony combined multi-relay selection and time slot resource allocation method is obviously superior to that of the termite colony and particle swarm combined multi-relay selection and time slot resource allocation method.
Fig. 7 is a graph of secret capacity of a Massive MIMO system as a function of user interference threshold. The simulation result shows that the secrecy capacity of the system tends to increase and then decrease with the increase of the user interference threshold, because although the total interference generated by the relay network increases with the increase of the user interference threshold, the signal-to-interference-and-noise ratio of the eavesdropper is greatly reduced, but the total interference generated also reduces the signal-to-interference-and-noise ratio of the user to a certain extent. Therefore, when the user interference threshold exceeds a certain range, the system security capacity does not increase with the increase of the interference threshold. The combined multi-relay selection and time slot resource allocation method adopting the quantum termite colony optimization mechanism can obtain larger system secret capacity, and the performance of the method is obviously superior to that of the combined multi-relay selection and time slot resource allocation method adopting the termite colony and the particle swarm.
The above description is further intended to describe the present invention in detail with reference to specific embodiments, and it should not be construed that the specific embodiments of the present invention are limited to these descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (1)

1. A method for combining multi-relay selection and time slot resource allocation of a large-scale MIMO system is characterized by comprising the following steps:
the method comprises the following steps: establishing a Massive MIMO cooperative communication system model, which specifically comprises the following steps:
the Massive MIMO cooperative communication system consists of a base station configured with M antennas, a user, L amplification-forwarding half-duplex relays and an eavesdropper, wherein the user, each relay and the eavesdropper are single-antenna equipment and share an authorized frequency band with a bandwidth of B with the base station, and all noises are assumed to be power spectral density of N0White Gaussian noise, the noise power σ2＝BN0Each frame of information transmission is divided into two different time slots: TS1 and TS2, in TS1, the base station sends signal to the relay, the user sends interference signal to the eavesdropper while the base station transmits information, then the jth relay rjReceived signal yjComprises the following steps:
Figure FDA0003010210340000011
wherein j is 1,2, …, L, sBSAnd suSignals of specific energy, p, transmitted for base stations and usersBSAnd puIs the transmit power of the base station and the user, w is the precoding matrix of the base station, ()HDenotes the conjugate transpose, hBS,jIndicating base station to relay rjChannel state information of hj,uIs a relay rjChannel state information to the user, njIs a relay rjReceived noise, will yjNormalization yields:
Figure FDA0003010210340000012
the method comprises the following steps of obtaining a vector norm by using a vector, and obtaining a variable modulus by using a linear equation, | to | represents the norm of the vector;
at TS1, the signal received by the eavesdropper
Figure FDA0003010210340000013
Comprises the following steps:
Figure FDA0003010210340000014
wherein h isBS,eIndicating channel state information from base station to eavesdropper, hu,eFor the channel state information of the user to the eavesdropper,
Figure FDA0003010210340000015
for the noise received by the eavesdropper at TS1, the signal-to-interference-and-noise ratio received by the eavesdropper during the transmission of information by the base station
Figure FDA0003010210340000016
Comprises the following steps:
Figure FDA0003010210340000017
when the base station transmits information, each relay collects the energy of the wireless signal received by the relay, and the relay rjEnergy E collected at TS1jComprises the following steps:
Ej＝η(pBS||wHhBS,j||2+pu|hj,u|2+σ2)αT
wherein eta is the energy acquisition rate, alpha is the time slot distribution coefficient, and T is the subframe duration of information transmission;
at TS2, some relays forward signals received at TS1 to users, and the rest relays to eavesdroppersTransmitting interference signal, selecting vector b ═ b through 0-1 relay1,b2,…,bL]Indicates the relay selection result if bjWhen 1, relay r is selectedjCarrying out data transmission, and sending a signal to a user by using the energy collected by the relay at the TS 1; if b isjWhen r is 0, then relay rjSending an interference signal to an eavesdropper; the total interference generated by the relay to the user must not exceed the interference threshold I of the userthRelay rjThe power control strategy employed is:
Figure FDA0003010210340000021
the method comprises the following steps that min { } represents the minimum value in a group of numbers, and N represents the total number of relays which send interference signals to an eavesdropper;
signals received by eavesdroppers at TS2
Figure FDA0003010210340000022
Comprises the following steps:
Figure FDA0003010210340000023
wherein h isj,eAnd hk,eAre respectively a relay rjAnd relay rkChannel state information to the eavesdropper, k ≠ j, s, 1,2, …, L, k ≠ jkIs a relay rkThe interference signal that is transmitted is,
Figure FDA0003010210340000024
for noise received by the eavesdropper at TS2, let
Figure FDA0003010210340000025
Selecting a variable b for relaying withkThe relevant variable, if bk＝0，
Figure FDA0003010210340000026
If b isk＝1，
Figure FDA0003010210340000027
Signal-to-interference-and-noise ratio received by eavesdropper in process of relay transmission information
Figure FDA0003010210340000028
Comprises the following steps:
Figure FDA0003010210340000029
wherein p isTSThe sum of the interference signal power received by the eavesdropper at TS2 can be specifically expressed as:
Figure FDA00030102103400000210
therefore, the signal-to-interference-and-noise ratio received by the eavesdropper in the information transmission process from the base station to the user is as follows:
Figure FDA00030102103400000211
wherein max {. means taking the maximum value of a set of numbers;
at TS2, by the self-interference cancellation method, the signal received by the user is:
Figure FDA00030102103400000212
wherein h isk,uIs a relay rkChannel state information to the user, nuFor the noise received by the user, the signal-to-interference-and-noise ratio gamma received by the user in the process of relaying the informationuComprises the following steps:
Figure FDA0003010210340000031
the secret capacity of the Massive MIMO relay system is:
R(b,α)＝max{(1-α)B[log2(1+γu)-log2(1+γe)],0}
step two: initializing quantum termite colony and system parameters, specifically:
setting the number of quantum termites in a quantum termite colony as H, the dimension of the positions of the quantum termites as D, wherein the D represents the dimension of the problem to be solved, binary bit coding is adopted for discrete variables to be solved, K binary bit coding is adopted for continuous variables, and then for the problem of joint multi-relay selection and time slot resource allocation of a Massive MIMO system, the dimension D of the positions of the quantum termites is L + K; using t to represent iteration times, the quantum position of the ith iteration of the i-th quantum termite is
Figure FDA0003010210340000032
Wherein the content of the first and second substances,
Figure FDA0003010210340000033
position of ith Quantum Termite
Figure FDA0003010210340000034
Can be obtained by measuring the quantum position of the quantum well, and the measurement equation is as follows:
Figure FDA0003010210340000035
wherein the content of the first and second substances,
Figure FDA0003010210340000036
is [0,1 ]]At the beginning, let t equal to 0, the initial position of quantum termite i is
Figure FDA0003010210340000037
The local optimum position of quantum termite i is
Figure FDA0003010210340000038
Step three: calculating an adaptive value of the position of the quantum termite, specifically:
position of ith quantum termite for the t iteration
Figure FDA0003010210340000039
Mapping to vector needing optimization of Massive MIMO cooperative communication system
Figure FDA00030102103400000310
Namely, the scheme of multi-relay selection and time slot resource allocation of the Massive MIMO system passes through the fitness function
Figure FDA00030102103400000311
Calculating the adaptive value of the quantum termite, wherein exp represents an exponential function, analyzing the adaptive values of all the quantum termites in the quantum termite colony, and recording the position with the minimum adaptive value searched by the ith quantum termite so far as a local optimal position
Figure FDA00030102103400000312
Marking the position with the minimum adaptive value searched by the whole quantum termite colony as the global optimal position
Figure FDA00030102103400000313
Step four: according to the evolution rule, the quantum position and the position of the quantum termite are updated, specifically:
the pheromone of the quantum termite is a function related to the adaptive value, and the adaptive value of the quantum termite i is determined according to the following formula
Figure FDA00030102103400000314
Conversion to the corresponding pheromone content
Figure FDA00030102103400000315
Figure FDA00030102103400000316
Wherein rho is [0,1 ]]As the evaporation rate of the pheromone, the evaporation rate,
Figure FDA00030102103400000317
for the pheromone content of the position where the quantum termite i is located in the last iteration, the quantum termite i obtains the corresponding learning neighborhood according to the following rule:
Figure FDA00030102103400000318
wherein the content of the first and second substances,
Figure FDA0003010210340000041
a set of labels for the ith quantum termite learning neighborhood,
Figure FDA0003010210340000042
for the dynamic search radius of the ith quantum termite,
Figure FDA0003010210340000043
the d-dimensional position of quantum termite l,
Figure FDA0003010210340000044
the pheromone content of the quantum termite l is represented, the number of the labels in the learning neighborhood label set of the quantum termite i represents the number of the quantum termite in the learning neighborhood of the quantum termite i, and the position of the quantum termite with the largest pheromone content in the learning neighborhood of the quantum termite i is recorded as the pheromone content of the quantum termite l
Figure FDA0003010210340000045
Quantum termite i evolves according to the following rules:
Figure FDA0003010210340000046
Figure FDA0003010210340000047
wherein the content of the first and second substances,
Figure FDA0003010210340000048
for the d-dimension quantum rotation angle of the ith quantum termite in the updated quantum termite colony,
Figure FDA0003010210340000049
for the updated d-dimensional quantum position of quantum termite i,
Figure FDA00030102103400000410
the data is an empty set,
Figure FDA00030102103400000411
is [0,1 ]]Uniform random number in between, epsilon is the variation probability of quantum position when quantum rotation angle is 0, abs (one.) represents absolute value, c1、c2、c3、c4、c5Is an influencing factor; c. C1、c2、c3Respectively representing the influence degrees of the local optimal position, the position with the maximum pheromone content in the learning neighborhood and the global optimal position of the ith quantum termite on the quantum rotation angle when the learning neighborhood is not empty; c. C4And c5Representing the influence degree of the local optimal position and the global optimal position of the ith quantum termite on the quantum rotation angle when the learning neighborhood is empty;
obtaining the updated position of the quantum termite i through a measurement equation according to the updated quantum position of the quantum termite i;
step five: obtaining the new position of the quantum termite i according to the mapping rule
Figure FDA00030102103400000412
Corresponding multiple relay selection and timeslot resource configuration vectors
Figure FDA00030102103400000413
Calculating the updated adaptive value of the quantum termite, and updating the local optimal position of the quantum termite through a greedy selection mechanism, wherein the process is as follows:
Figure FDA00030102103400000414
recording the local optimal position with the minimum adaptive value in the updated quantum termite colony as the global optimal position
Figure FDA00030102103400000415
Step six: if the iteration times are less than the preset maximum iteration times, making t equal to t +1, and returning to the fourth step; otherwise, stopping iteration and outputting the global optimal position of the quantum termite colony
Figure FDA00030102103400000416
And obtaining the optimal combined multi-relay selection and time slot resource allocation scheme of the Massive MIMO system through the mapping rule.
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